ANALYZE QUEUE PERFORMANCE USING ARENA 16.0 SOFTWARE (Z Cinema Case Study)

Queues are often found in everyday life, such as waiting for cinema counters, check-in queues at airports and self-service cashier queues. Service facilities for a long time cause queues because they cannot meet the needs or capacity, so facility users have to wait. As in the example of the case of Cinema Z in Solo City has a maximum queue time of 13.6 minutes with a maximum number of queues of 11 people until served. This can cause customers to wait long enough to get movie tickets. This problem can be overcome by the need to manage the counter operator so that it can regulate when to open a new counter so that there is no queue. This research discusses the problem of queuing models at Cinema Z in Solo, Central Java Province. From the results of the analysis that can be known the performance values of the queuing system such as waiting time, number in, number out, dan number waiting.


Introduction
Today's life, all societies want fast-paced activities and services. Speed and time savings are very supportive for carrying out all life activities. One of the activities that requires speed and time savings is queuing. In everyday life a person often experiences the thing to wait in line for a long time and this is something that causes boredom. And it's great to get a service that doesn't have to wait. Waiting for long queues can be caused by a lack of facilities to serve the community or the number of service counters that exist inadequate to serve the community, as well as the lack of responsiveness of servants to serve the community or consumers.
A queuing process is a process related to the arrival of a customer at a service facility, then waiting in a line (queue) if all the waiters are busy, and finally leaving the facility (Taha, H.A. 1997) . A queuing system is a set of customers, waiters and a rule governing the arrival of customers and the processing of the problem (Bronson, Richard, and Hans J. Wospakrik 1982). Suad Husna (1982) in his book entitled "Queuing Theory", it is said that one of the proper ways to solve the queuing problem is to use the method of simulating the whole problem to design an experiment that will mimic as closely as possible the actual state of affairs and then observe what will happen.
This simulation method is one of the effective methods to solve the queue problem. Simulation is a methodology for carrying out experiments using models from one real system (Siagian, 1987). According Hasan (2002), Simulation is a model of decision making by modeling or using the true image of a real-world life system without having to experience it in its true state. In research conducted at cinema Z using Arena 16.0 software to create a simulation model of the customer queue system in the cinema so as to reduce the occurrence of queues. Cinema managers are expected to implement a policy when to open new counters so that there are no idle or too busy counters so that service for cinema customers is maximized.

Methodology
Based on the background that has been presented, the problem of optimizing services in cinema Z is formulated, further determining the goals, limitations and collecting data. At the data collection stage, direct observations are carried out at the cinema counter Z. Direct observation to find out the real system conditions at the service facility. The data collected are data on arrival time, service time, and end time served at Z cinema. Furthermore, data processing and analysis is carried out using Arena 16.0 software. According to Rossetti (2016), simulation method as in Figure 1. The value of the Number In and Number Out items shows that the number of customers that can be served by the system reaches 100%, which is 100 people.
b. The value of the wait time item can be known that the customer waits in the queue before the service at the counter for a maximum of 13.68 minutes and at the proposed model of 4.3 minutes. c.
Based on the results of the item, the total time of counter employees serving customers with a minimum length of time is 1,326 minutes and the maximum is 17,772 minutes. While in the proposed model, the minimum service is 1.23 minutes and maximum 8.46 minutes. d.
The waiting time of customers (waiting time) at the counter is a maximum of 13.68 minutes, while in the proposed simulation model, the maximum waiting time is 4.3 minutes.
e. Many queues (number waiting) at the ticket counter are a maximum of 11 people. While in the proposal a maximum queue of 4 people.

Conclusion
Based on the results of the analysis of the cinema queue simulation model in the current conditions, an improvement proposal was made to improve the cinema ticket queue. The proposal given is the addition of 1 e-ticket counter.
For future research, researcher may retrieve data for the whole day instead of just 5 hours, so that the simulation created can represent a real system. Also, researcher can evaluate the proposed improvements, whether the addition of 1 e-ticket counter is effective.